Logical Graph Research Articles

An event logic graph for geographic environment observation planning in disaster chain monitoring

Effective geographic environment observation planning is the key to obtain disaster monitoring and warning information. The previous researches can only make observation plans for a single disaster at some specific stages. They are difficult to apply to the dynamic evolution of the disaster chain. Timely and comprehensive geographic environment observation planning is urgently needed to provide high-value monitoring data for the identification and response of secondary disaster chains. Event logic graph (ELG) shows great potential in evolutionary law expression and chain event reasoning. Therefore, this study proposed an observation ELG (OELG), in which events and their logical relationships are modeled as nodes and edges to express the occurrence and development motivation of observation events. The disaster chain observation planning can be transformed into the reasoning of potential continuous observation events. Subsequently, an OELG-based geographic environment observation planning framework was proposed, which realizes the construction, instantiation, and plan reasoning of OELG. The observation planning experiment was carried out taking the flood disaster chain that occurred in Beijing, China and Nordrhein-Westfalen, Germany as examples. The results show that OELG can generate disaster chain observation plan more timely, comprehensively, and continuously than other models, thus providing support for disaster chain risk monitoring and emergency response.

Heterogeneous-branch integration framework: Introducing first-order predicate logic in Logical Reasoning Question Answering

The logical reasoning question-answering is a critical task in natural language processing, as it equips models with human-like logical reasoning intelligence. Existing approaches focus on extracting and leveraging the hidden logical structures within text. However, previous works explore partial logical relationships and neglect the holistic extraction within the text. Moreover, they struggle to fully model logical connections, including long-distance dependencies and local topology information. To address these issues, we propose a novel heterogeneous-branch integration framework. Our framework is based on first-order predicate logic theory and consists of three primary components. First, we construct two heterogeneous logical graphs to model logical relationships within and between propositions. Second, we propose a novel Graph-Masked Transformer with a novel graph-masked multi-head attention mechanism to enable distant node interactions and local sparse relationship modeling. Third, we propose a novel multi-branch fusion module to integrate information from multiple sources and generate answer predictions. The proposed heterogeneous-branch integration framework outperforms the VDGN method by 2.73% in accuracy on the ReClor dataset and 2.15% on the LogiQA dataset. Our code and models will be made available at https://github.com/starry-y/HBI.

CGKPN: Cross-Graph Knowledge Propagation Network with Adaptive Connection for Reasoning-Based Machine Reading Comprehension

The task of machine reading comprehension (MRC) is to enable machine to read and understand a piece of text and then answer the corresponding question correctly. This task requires machine to not only be able to perform semantic understanding but also possess logical reasoning capabilities. Just like human reading, it involves thinking about the text from two interacting perspectives of semantics and logic. However, previous methods based on reading comprehension either consider only the logical structure of the text or only the semantic structure of the text and cannot simultaneously balance semantic understanding and logical reasoning. This single form of reasoning cannot make the machine fully understand the meaning of the text. Additionally, the issue of sparsity in composition presents a significant challenge for models that rely on graph-based reasoning. To this end, a cross-graph knowledge propagation network (CGKPN) with adaptive connection is presented to address the above issues. The model first performs self-view node embedding on the constructed logical graph and semantic graph to update the representations of the graphs. Specifically, a relevance matrix between nodes is introduced to adaptively adjust node connections in response to the challenge posed by sparse graph. Subsequently, CGKPN conducts cross-graph knowledge propagation on nodes that are identical in both graphs, effectively resolving conflicts arising from identical nodes in different views, and enabling the model to better integrate the logical and semantic relationships of the text through efficient interaction. Experiments on the two MRC datasets ReClor and LogiQA indicate the superior performance of our proposed model CGKPN compared to other existing baselines.

Efficient implementation of majority-inverter graph logic and arithmetic functions with memristor arrays

Efficient implementation of majority-inverter graph logic and arithmetic functions with memristor arrays

Feature Selection Techniques for Enhancing Credit Card Fraud Detection Performance: A Hybrid Metaheuristic Approach Using Nature-Inspired Algorithms

Abstract: The surge in credit card usage has led to a parallel increase in fraudulent transactions, necessitating advanced detection systems. Traditional methods encounter challenges with high-dimensional and imbalanced datasets. This paper proposes a comprehensive approach integrating metaheuristic algorithms and deep learning techniques to enhance fraud detection accuracy. The first contribution, the Rock Hyrax Swarm Optimization Feature Selection (RHSOFS) algorithm, draws inspiration from the collective behaviors of rock hyrax swarms to effectively select relevant features from high-dimensional datasets. RHSOFS identifies an optimal subset of features critical for fraud detection through supervised machine learning. Complementing this, the second contribution leverages a hybrid deep learning model. Beginning by organizing transactional data and constructing a Logical Graph of Behavior Profile (LGBP) to abstract transaction details, the Modified Butterfly Optimization Algorithm (MBOA) selects important features from the dataset. The hybrid model, integrating Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), establishes rational connections between transactional characteristics, enhancing detection performance. To evaluate the approach, comparative efficiency analyses against existing methods, including Differential Evolutionary Feature Selection (DEFS), Genetic Algorithm Feature Selection (GAFS), Particle Swarm Optimization Feature Selection (PSOFS), and Ant Colony Optimization Feature Selection (ACOFS), are conducted. Results demonstrate the superiority of the approach in terms of both reliability and recognition rate, validated through rigorous statistical testing. The proposed hybrid approach signifies a significant advancement in credit card fraud detection systems, offering enhanced accuracy and efficiency in combating fraudulent activities amidst the growing complexity of transactional datasets.

A Hazardous Chemical Accident Prevention Method Based on Event Logic Graph

A Hazardous Chemical Accident Prevention Method Based on Event Logic Graph

A hazardous chemical accident prevention method based on event logic graphs

A hazardous chemical accident prevention method based on event logic graphs

Application of logical methods in expert analysis of automotive engine failures in operation

Problem. It is known that the causes of failures in automotive engines can be determined based on existing experience in studying various engine malfunctions. However, an analysis of known methods for determining the causes of car engine failures shows that their use in practice, including in diagnosing the technical condition of engines, requires a lot of work and highly qualified personnel that in many cases is practically ineffective. Logical-probabilistic methods are also known, including those based on fault tree analysis. However, it is not possible to use such models and methods in searching for the c auses of engine failure. Goal. The purpose of the study is to create a methodology for determining the causes of faults and failures in engine operation, which can be applied with help of a logical way and applicable not only by the specialists with expert level, but also the wide qualification. Methodology. The solution to this problem was found in several stages. Initially, by structuring the symptoms, a fault tree was constructed that logically describes the cause-and-effect relationships between the failure event and the original damage that caused it. Data from expert studies of engine failures were used to construct the graph. This was done for each of the engine failure types selected for analysis. Next, a modified (inverted) fault tree was developed for a finite number of selected failure types. Results. Obtained logical graphs allow you to perform a simple logical analysis in the opposite direction from the generally accepted one, from the event of a system failure to the basic damage events that cause it. After obtaining logical graphs for each type of failure, a modified engine fault tree can be compiled, common to the considered types of failures. Originality. The proposed method was widely used previously in the study of reliability of various technical systems, however, this method for the first time has been proposed to determine the causes of engine failures. Practical value. As a result of using the proposed methodology in practice, it it became possible to determine the causes of failure in the engine with enough reliability for practice and minimal time expenditure.

Bounded model checking for interval probabilistic timed graph transformation systems against properties of probabilistic metric temporal graph logic

Cyber-physical systems often encompass complex concurrent behavior with timing constraints and probabilistic failures on demand. The analysis whether such systems with probabilistic timed behavior adhere to a given specification is essential. The formalism of Interval Probabilistic Timed Graph Transformation Systems (IPTGTSs) is often a suitable choice to model cyber-physical systems because (a) its rule-based approach to graph transformation can capture a wide range of system's structure dynamics when the states of the system can be represented by graphs while (b) it employs interval specifications for probabilistic behavior as well as lower and upper bounds on delays of steps to support systems where precise probabilities and delays are not known or may change during the runtime of the system. Probabilistic Metric Temporal Graph Logic (PMTGL) has been introduced as a powerful specification language to express worst-case/best-case probabilistic timed requirements such as actor-based soft deadlines using (a) path properties relying on its Metric Temporal Graph Logic fragment to track individual graph elements and (b) an operator inherited from Probabilistic Timed Computation Tree Logic to express worst-case/best-case probabilistic requirements identifying worst-case/best-case resolutions of non-determinism. Bounded Model Checking (BMC) support for Probabilistic Timed Graph Transformation Systems (PTGTSs) w.r.t. properties specified using PMTGL has been already presented. However, for IPTGTSs no analysis support w.r.t. PMTGL properties has been developed for stating metric temporal properties on identified subgraphs and their structural changes over time.In this paper, we adapt the BMC approach developed for PTGTSs to the case of IPTGTSs extending modeling and analysis support to the usage of probability intervals more appropriately covering cyber-physical systems where probabilistic effects cannot be specified precisely and need to be approximated instead. In our evaluation, we apply an implementation of our BMC approach in AutoGraph to a novel running example demonstrating the effect of using probability intervals instead of precise probability values.

Discourse-Aware Graph Networks for Textual Logical Reasoning.

Textual logical reasoning, especially question-answering (QA) tasks with logical reasoning, requires awareness of particular logical structures. The passage-level logical relations represent entailment or contradiction between propositional units (e.g., a concluding sentence). However, such structures are unexplored as current QA systems focus on entity-based relations. In this work, we propose logic structural-constraint modeling to solve the logical reasoning QA and introduce discourse-aware graph networks (DAGNs). The networks first construct logic graphs leveraging in-line discourse connectives and generic logic theories, then learn logic representations by end-to-end evolving the logic relations with an edge-reasoning mechanism and updating the graph features. This pipeline is applied to a general encoder, whose fundamental features are joined with the high-level logic features for answer prediction. Experiments on three textual logical reasoning datasets demonstrate the reasonability of the logical structures built in DAGNs and the effectiveness of the learned logic features. Moreover, zero-shot transfer results show the features' generality to unseen logical texts.

The disjoint path cover in the data center network HSDC with prescribed vertices in each path

The n-dimensional HSDC with the logic graph Hn is one of the most attractive server-centric data center networks for high incremental scalability. The routing design in network topology is very important for information transmission. In particular, the application of disjoint path covers can solve various problems such as program code optimization and mapping parallel programs to parallel structures. Hn consists of 2n cliques and each two vertices in a clique are adjacent. For any vertex sets {x,u} and {y,v} in W and B, respectively, and any two disjoint vertex sets A1,A2 in some clique, where (W,B) is a bipartition of Hn, in this paper, we prove that, when |Ai|≠1 and Ai∩{x,y,u,v}=∅ for i=1,2, if |A1∪A2|≤n−3, then there exists an (x,y)-path P1 and a (u,v)-path P2 in Hn passing through A1 and A2, respectively, such that P1 and P2 have no common vertices and P1∪P2 contains all vertices of Hn. Then P1∪P2 is called the 2-disjoint path cover in Hn and we propose the efficient algorithms for the path design. Furthermore, we prove that our main result is optimal.

Constructing Multiple CISTs on BCube-Based Data Center Networks in the Occurrence of Switch Failures

The scale of data center networks (DCNs) has grown rapidly with the increasing popularity of cloud computing, data explosion, and the dramatic drop in setup costs. Thus, inevitable component failures (including switches and servers) will become more frequent. A DCN requires maintaining regular and reliable operation and providing efficient routing algorithms for transmitting data between servers. Particularly, fault-tolerant routing is necessary. Recently, constructing completely independent spanning trees (CISTs) on DCNs has received much attention as a dual-CIST (i.e., two CISTs) suffices to configure protection routing, which is a fault-tolerant routing. Moreover, the protection routing can additionally realize a secure mechanism if it is configured by more CISTs. BCube is a server-centric DCN with many advantages, and many variations were deformed from BCube with application requirements, such as RCube and RRect. In this paper, we provide a unified framework called BCube-based DCN (BDCN) that integrates the representation of the logic graphs of DCNs mentioned above, facilitating consistent algorithms' design. Then, we develop efficient algorithms to construct multiple CISTs on BDCN under the consideration of switch failures. Note that this is the first study that constructs multiple CISTs in DCNs with switch failures. Finally, using standard metrics, such as average path length (APL) and transmission failure rate (TFR), we evaluate the performance of the fault-tolerant routing through experiments.

Enumerating Independent Linear Inferences

A linear inference is a valid inequality of Boolean algebra in which each variable occurs at most once on each side. In this work we leverage recently developed graphical representations of linear formulae to build an implementation that is capable of more efficiently searching for switch-medial-independent inferences. We use it to find four `minimal' 8-variable independent inferences and also prove that no smaller ones exist; in contrast, a previous approach based directly on formulae reached computational limits already at 7 variables. Two of these new inferences derive some previously found independent linear inferences. The other two (which are dual) exhibit structure seemingly beyond the scope of previous approaches we are aware of; in particular, their existence contradicts a conjecture of Das and Strassburger. We were also able to identify 10 minimal 9-variable linear inferences independent of all the aforementioned inferences, comprising 5 dual pairs, and present applications of our implementation to recent `graph logics'.

A Novel Scene Matching Navigation System for UAVs Based on Vision/Inertial Fusion

Scene matching navigation system (SMNS) remains challenging in many navigation tasks, which rely heavily on accuracy, computational efficiency, and robustness. Due to the different generation conditions of the matching images, it is difficult for traditional methods to cover every aspect of the three navigation performances. This article aims at developing an accurate, fast, and robust SMNS based on vision/inertial fusion to provide complete navigation information for unmanned aerial vehicles (UAVs). Using the mechanization results of the low-cost micro-electro-mechanical system (MEMS), the proposed system first completes the georeferencing of the real-time aerial images, in which the projection errors are reduced greatly by introducing an optimized factor to the homography matrix. Then, applying a robust noise processing strategy, an improved feature extraction algorithm is designed to eliminate most of the features that vary with climate, time, and season, which lays a solid foundation for the accuracy of the following matching procedure. Under the framework of the SMNS, a novel matching strategy based on logic graphs is designed, which can facilitate the matching procedure. Eventually, by combining the mechanization results of the MEMS and the matching results of the SMNS, the proposed system can provide complete navigation results. Experiments in typical and complex scenarios are carried out, respectively, to verify the effectiveness and robustness of the proposed system. Experimental results demonstrate that the proposed SMNS possesses accuracy, computational efficiency, and robustness, which outperforms the state-of-the-art strategies [i.e., histogram of orientated phase congruency (HOPC), chanel feature of orientated gradient (CFOG), phase congruency (PC)] in terms of matching aerial and satellite images.

Evaluation diversity for graph conditions

Graphs are used as a universal data structure in various domains. Sets of graphs (and likewise graph morphisms) can be specified using, e.g., the graph logic ▪ of Graph Conditions (GCs). The evaluation of a graph against such a GC results in a Boolean satisfaction judgement on whether the graph is specified by the GC. The graph logic ▪ is known to be as expressive as first-order logic on graphs. However, since infinitely many graphs exist, there are also infinitely many evaluations for each given GC. To support GC validation, testing, debugging, and repair, a suitable synthesis procedure generating a complete compact overview of how a given GC may be evaluated for possibly varying graphs is called for.In a previous paper, we generated such an overview for a given GC in the form of a complete finite set of diverse evaluations for varying associated graphs formally given by so called Evaluation Trees (ETs). Each of these ETs concretely describes how its associated graph is evaluated against the given GC by recording the executed evaluation steps. Moreover, these generated ETs and the given GC for which they are generated have the same underlying structure easing comprehensibility of the represented evaluation steps. The returned ETs are complete since each possible ET subsumes one of the returned ETs and diverse by not containing superfluous ETs subsuming smaller ETs.We now extend and refine this approach still solving the ET synthesis problem by (a) extending the graph logic GL allowing for the specification of a minimal number of graph patterns to be contained in specified graphs, (b) provide means to scale the size of the generated ETs up to a user-provided bound allowing for the generation of not just minimal ETs, (c) record the order of evaluations steps also for operators where the evaluation but not the operator itself defines such an order, and (d) generate ETs recording combinations of reasons for (non-)satisfaction of GCs where only single reasons were recorded before.

Research on the construction and implementation of power grid fault handling knowledge graphs

Power grid fault disposal has important guiding significance for the efficient and orderly emergency work of power grid accidents. The knowledge graph technology is used to extract, express and manage the fault disposal information. It is also used to assist dispatchers in fault handling, which can effectively improve the power grid emergency handling capacity and dispatching intelligence level. The unstructured content in the form of a large number of texts in the power grid dispatching operation link is condensed into a structured knowledge network that can be expressed, operated and reasoned. For example, operating procedures, disposal plans, dispatching rules, etc. A top-down and bottom-up knowledge graph construction method for power grid fault handling is proposed. The knowledge graphs are divided into three categories: entity graph of power grid equipment, logic graph of fault handling and case graph. The data and technical means required for graph construction are described. In the process of practice, the knowledge organization structure, notion types, and relationships between notions of the knowledge graphs are defined to form the model layer of knowledge graph. Finally, according to the function realization of each link, the solution ideas and key technologies are analyzed. So as to realize fault information analysis and discrimination based on knowledge atlas, intelligent aided decision-making and multi-dimensional human–computer interaction.

Uncovering illicit supply networks and their interfaces to licit counterparts through graph-theoretic algorithms

The rapid market growth of different illicit trades in recent years can be attributed to their discreet, yet effective, supply chains. This article presents a graph-theoretic approach for investigating the composition of illicit supply networks using limited information. Two key steps constitute our strategy. The first is the construction of a broad network that comprises entities suspected of participating in the illicit supply chain. Two intriguing concepts are involved here: unification of alternate Bills-of-materials and identification of entities positioned at the interface of licit and illicit supply chain; logical graph representation and graph matching techniques are applied to achieve those objectives. In the second step, we search for a set of dissimilar supply chain structures that criminals might likely adopt. We provide an integer linear programming formulation as well as a graph-theoretic representation for this problem, the latter of which leads us to a new variant of Steiner Tree problem: Generalized Group Steiner Tree Problem. Additionally, a three-step algorithmic approach of extracting single (cheapest), multiple and dissimilar trees is proposed to solve the problem. We conclude this work with a semi-real case study on counterfeit footwear to illustrate the utility of our approach in uncovering illicit trades. We also present extensive numerical studies to demonstrate scalability of our algorithms.

QuTIE: quantum optimization for target identification by enzymes.

Target identification by enzymes (TIE) problem aims to identify the set of enzymes in a given metabolic network, such that their inhibition eliminates a given set of target compounds associated with a disease while incurring minimum damage to the rest of the compounds. This is a NP-hard problem, and thus optimal solutions using classical computers fail to scale to large metabolic networks. In this article, we develop the first quantum optimization solution, called QuTIE (quantum optimization for target identification by enzymes), to this NP-hard problem. We do that by developing an equivalent formulation of the TIE problem in quadratic unconstrained binary optimization form. We then map it to a logical graph, and embed the logical graph on a quantum hardware graph. Our experimental results on 27 metabolic networks from Escherichia coli, Homo sapiens, and Mus musculus show that QuTIE yields solutions that are optimal or almost optimal. Our experiments also demonstrate that QuTIE can successfully identify enzyme targets already verified in wet-lab experiments for 14 major disease classes. Code and sample data are available at: https://github.com/ngominhhoang/Quantum-Target-Identification-by-Enzymes.

Dialogue Logic Aware and Key Utterance Decoupling Model for Multi-Party Dialogue Reading Comprehension

Multi-party dialogue machine reading comprehension (MRC) brings an unprecedented challenge due to the multiple speakers and the complex discourse linkages among speaker-aware utterances. The majority of current methods only consider the textual aspects of dialogue situations, and pay little attention to crucial speaker-aware cues. This prevents a model from capturing the speaker’s intention and important discourse information for questions in a complex discourse relationship, leading to the model giving wrong answers. In this paper, we construct a dialogue logic graph module by the relational graph convolutional network (R-GCN) to structure the dialogue information, and design a speaker prediction task to enhance the ability to capture discourse logic. Additionally, we construct a key utterance information decoupling module that focuses on the key discourse information flow involve questions, and filters out noise information. Extensive experiments FriendsQA and Molweni show that our approach outperforms competitive baselines and current state-of-the-art models, especially when dealing with more rounds of dialogue and questions involving people, events and time.

The construction and application of event logic graph for pedestrian flow evacuation in typical scenarios

An event logic graph is a kind of knowledge mapping technology for knowledge inference and simulation analysis, which takes events as the core and portrays the hierarchical system and logical evolution pattern between events. In order to apply it to further improve the accuracy of related studies, such as pedestrian flow evacuation, simulation model optimization and risk prediction. In this paper, we use social network resources, media resources and journal database resources to build our corpus and adopt the explicit event relationship extraction method based on syntactic dependency and the implicit event relationship extraction method based on BERT+Bi-LSTM+Attention+Softmax for the characteristics of explicit event relationship and implicit event relationship, respectively. This paper constructs a pedestrian flow evacuation matter mapping for three typical scenarios and discusses its application path. It is found that once a sound knowledge base of logical reasoning and event logic graph is established, both research on optimization of pedestrian flow evacuation simulation models and research on identification and assessment of pedestrian flow evacuation safety risks will receive excellent support.